About the Program

The Department of Industrial Engineering and Operations Research (IEOR) offers three graduate programs: a Master of Engineering (MEng), a Master of Science (MS), and a PhD. These programs have been developed to meet the needs of individuals with backgrounds in engineering or the mathematical sciences who wish to enhance their knowledge of the theory, development, and use of quantitative models for design, analysis, risk management, and decision making in complex systems in the industrial, service, or public sectors, including energy systems, supply chains, healthcare systems, and financial systems. Students may concentrate on theoretical studies in preparation for doctoral-level research, or on applications of state-of-the-art techniques to real world problems.

Master of Engineering (MEng)

The MEng is a professional, full-time, accelerated professional master's degree program.  Students learn advanced techniques in IEOR and skills that prepare them to lead teams in developing new engineering solutions: skills in managing complex projects, motivating people, and directing financial and operational matters.

Master of Science (MS)

The MS is a full-time technical master's degree program. Students focus on both the theory of IEOR techniques, and application of those techniques. The MS is a terminal degree, meaning that students enrolled in the MS program do not typically continue further into the IEOR PhD program. Participants in the program are self-funded; the Department of IEOR does not offer funding and students will not be eligible for ASE (academic student employment) appointments funded by the department.

Doctor of Philosophy (PhD)

The paramount requirement of a doctoral degree is the successful completion of a thesis on a subject within Industrial Engineering and Operations Research. Research areas may include the investigation of the mathematical foundations of and computational methods for optimization or stochastic models, including risk analysis. Research also may be undertaken to develop methodologies for the design, planning, and/or control of systems in a variety of application domains, including supply chains, energy systems, healthcare systems, and financial systems.

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Admissions

Admission to the University

Minimum Requirements for Admission

The following minimum requirements apply to all graduate programs and will be verified by the Graduate Division:

1. A bachelor’s degree or recognized equivalent from an accredited institution;
2. A grade point average of B or better (3.0);
3. If the applicant comes from a country or political entity (e.g., Quebec) where English is not the official language, adequate proficiency in English to do graduate work, as evidenced by a TOEFL score of at least 90 on the iBT test, 570 on the paper-and-pencil test, or an IELTS Band score of at least 7 (note that individual programs may set higher levels for any of these); and
4. Sufficient undergraduate training to do graduate work in the given field.

Applicants Who Already Hold a Graduate Degree

The Graduate Council views academic degrees not as vocational training certificates, but as evidence of broad training in research methods, independent study, and articulation of learning. Therefore, applicants who already have academic graduate degrees should be able to pursue new subject matter at an advanced level without need to enroll in a related or similar graduate program.

Programs may consider students for an additional academic master’s or professional master’s degree only if the additional degree is in a distinctly different field.

Applicants admitted to a doctoral program that requires a master’s degree to be earned at Berkeley as a prerequisite (even though the applicant already has a master’s degree from another institution in the same or a closely allied field of study) will be permitted to undertake the second master’s degree, despite the overlap in field.

The Graduate Division will admit students for a second doctoral degree only if they meet the following guidelines:

1. Applicants with doctoral degrees may be admitted for an additional doctoral degree only if that degree program is in a general area of knowledge distinctly different from the field in which they earned their original degree. For example, a physics PhD could be admitted to a doctoral degree program in music or history; however, a student with a doctoral degree in mathematics would not be permitted to add a PhD in statistics.
2. Applicants who hold the PhD degree may be admitted to a professional doctorate or professional master’s degree program if there is no duplication of training involved.

Applicants may apply only to one single degree program or one concurrent degree program per admission cycle.

Required Documents for Applications

1. Transcripts: Applicants may upload unofficial transcripts with your application for the departmental initial review. If the applicant is admitted, then official transcripts of all college-level work will be required. Official transcripts must be in sealed envelopes as issued by the school(s) attended. If you have attended Berkeley, upload your unofficial transcript with your application for the departmental initial review. If you are admitted, an official transcript with evidence of degree conferral will not be required.
2. Letters of recommendation: Applicants may request online letters of recommendation through the online application system. Hard copies of recommendation letters must be sent directly to the program, not the Graduate Division.
3. Evidence of English language proficiency: All applicants from countries or political entities in which the official language is not English are required to submit official evidence of English language proficiency. This applies to applicants from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, most European countries, and Quebec (Canada). However, applicants who, at the time of application, have already completed at least one year of full-time academic course work with grades of B or better at a US university may submit an official transcript from the US university to fulfill this requirement. The following courses will not fulfill this requirement:
   • courses in English as a Second Language,
   • courses conducted in a language other than English,
   • courses that will be completed after the application is submitted, and
   • courses of a non-academic nature.

If applicants have previously been denied admission to Berkeley on the basis of their English language proficiency, they must submit new test scores that meet the current minimum from one of the standardized tests.

Where to Apply

Visit the Berkeley Graduate Division application page . 

Doctoral Degree Requirements

Normative Time Requirements

Normative Time to Advancement

Total normative time to advancement is 2-3 years.

Step I: This process normally takes 1 year (to take the entrance exam).

Step II: After passing the preliminary or entrance exam, students prepare for their PhD oral qualifying examination. This step lasts one to two years. With the successful passing of the orals, students are advanced to candidacy for the PhD degree.

Normative Time in Candidacy

Step III: Students undertake research for the PhD dissertation under a three-person committee in charge of their research and dissertation. The students then write the dissertation based on the results of this research. On completion of the research, workshops and approval of the dissertation by the committee, the students are awarded the doctorate.

Total Normative Time

Total normative time is 5-6 years or 10-12 semesters.

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Time to Advancement

Doctoral Entrance Exam

Every doctoral student is required to take the doctoral entrance examination. Students entering without an MS degree are required to complete all MS degree requirements, and may do so by completing the MS course requirements and passing the doctoral entrance exam.

The entrance examination consists of three parts:

1. An optimization exam: Students are required to take IND ENG 262A and at least one other course in Group A (see below) to be prepared for this exam.
2. A stochastic processes exam: Students are required to take IND ENG 263A and at least one other course in Group B (see below) to be prepared for this exam.
3. An exam on modeling and applied operations research: Students are required to take two courses in Group C (see below to be prepared for this exam.

All required courses for the doctoral entrance examination must be taken for a letter grade.

The entrance examination will be offered near the end of every spring semester, approximately one week before finals. Passing the entrance examination is based on both superior performances on all parts of the exam, and on previous coursework. Students are required to take the entire exam at the same time. In order to take the exam, students are expected to perform sufficiently well in their first year courses. During the middle of the spring semester, a faculty committee will review the performance of first year doctoral students, and students who have performed sufficiently well on their coursework (so that a superior performance on all parts of the exam will lead to passing) will be permitted to take the exam.

All students who would like to be considered for the doctoral program are expected to take this exam no later than their third semester in this department. In particular, students who enter in the fall are expected to take the exam at the end of the spring semester in the same academic year.

Curriculum

Advanced undergraduate courses in linear algebra and real analysis (equivalent to MATH 110 and MATH 104) are prerequisites for the PhD program. Students who have not taken these courses prior to entering the graduate program are required to do so during their first year.

Some students have specific research interests and goals when they enter a doctoral program; for others, these interests develop in the process of taking courses and preparing for the entrance examination. In either case, it is imperative that students begin their research as soon as possible after completing their entrance examination. One of the important initial steps in this process is finding a faculty member who will agree to supervise the dissertation (thesis adviser). Every student is required to complete at least one unit of independent study with a faculty member each semester after passing the entrance examination until finding a thesis adviser.

A minimum of nine graduate courses are required in the major, including those taken prior to the entrance examination. Usually, these are courses taken in this department, but to a very limited extent, courses taken in other departments or at other institutions may be counted as part of this requirement. These courses should provide depth in the student's probable research area.

In addition, course work is required in two minor areas. This is a College of Engineering requirement, which specifies that two or three courses (of advanced undergraduate or graduate level) typically represent a minimum program for a minor. This loose wording reflects the diverse needs of the College. In this department, each minor must consist of six units at the graduate level, at least three of which must be taken for a letter grade. A minor may serve either to strengthen theoretical foundations (e.g., measure-theoretic probability theory), or as an area of application (e.g., transportation). At most one course of one minor can be a course from within this department, as long as this course is distinct from the major. Both minors should be selected to strengthen the student's background in his or her research area, and subject to the approval of the head graduate adviser. Graduate courses at other institutions may make up part of a minor if the subject matter is appropriate.

The thesis adviser, once known, should be consulted about all matters regarding the program of study.

Coursework is comprised of an approved study list based on the student’s research interest, which must include the following:

Course List

Code	Title	Units
IND ENG 262A	Mathematical Programming I	4
IND ENG 263A	Applied Stochastic Process I	4
IND ENG 298	Group Studies, Seminars, or Group Research	1
Group A: Optimization: Select a minimum of one of the following:		3
IND ENG 262B	Mathematical Programming II
IND ENG 264	Computational Optimization
IND ENG 266	Network Flows and Graphs
IND ENG 269	Integer Programming and Combinatorial Optimization
Group B : Stochastic Modeling: Select a minimum of one of the following:		3
IND ENG 231	Introduction to Data Modeling, Statistics, and System Simulation
IND ENG 261	Experimenting with Simulated Systems
IND ENG 263B	Applied Stochastic Process II
IND ENG 267	Queueing Theory
IND ENG 268	Applied Dynamic Programming
Group C: Modeling and Applied Operations Research: Select a minimum of two of the following: 1		6
IND ENG 215	Analysis and Design of Databases
IND ENG 220	Economics and Dynamics of Production
IND ENG 221	Introduction to Financial Engineering
IND ENG 250	Introduction to Production Planning and Logistics Models
IND ENG 251	Facilities Design and Logistics
IND ENG 253	Supply Chain Operation and Management
IND ENG 254	Production and Inventory Systems
IND ENG 265	Learning and Optimization
IND ENG 268	Applied Dynamic Programming

1

In addition to the courses listed here, many occasionally-offered 290 series courses fit into this category, such as IND ENG 290A (Dynamic Production Theory and Planning Models) and IND ENG 290R (Topics in Risk Theory; check with the head graduate adviser about specific courses which may be approved.

Foreign Language(s)

In addition to English, the program does not require other language.

Qualifying Examination (QE)

The qualifying examination is an oral examination administered by four faculty members. Three of these faculties members are required to be IEOR faculty members and the fourth committee member must be from outside the department, and have expertise in one of the student’s minor areas of study. Students are expected to take the qualifying exam within three semesters after completing the doctoral entrance exam. Priority in department funding (especially NRTs) will be given to students who have passed their doctoral entrance exams and are in their third, fourth and fifth semesters. Although it is necessary for a student to identify a potential research area and some potential dissertation topics in order to complete this exam, it is not necessary for the student to do a substantial amount of research in the area of the examination.

The student is required to have completed or be currently enrolled in courses that will complete at least one of the two minors at the time of the qualifying examination. At least one of the minors completed or being completed at the time of the examination must consist entirely of courses from outside the department. In addition, at the time of the qualifying examination, the student is required to have a specific plan for completing the other minor within two semesters.

Prior to the exam, the student is required to identify a research area (broadly defined) in which he or she will be able to demonstrate expertise during the oral part of the examination. In addition, the student must be prepared to demonstrate expertise in one minor field. The objective of the exam is to assess the student’s ability to demonstrate knowledge in a broad research area, and to identify potential research topics within this area.

At least six weeks prior to the approximate date of the qualifying examination, the student needs to begin to arrange for Graduate Division approval of the exam committee. The student needs to pick up the appropriate form from the student affairs officer. Once the date and the exam committee are decided upon, the student must also request a room in which the exam can be held. Meanwhile, the student should prepare a list of topics, called a syllabus, which will form the basis of the exam. The syllabus should include topics from the three subject areas to be listed on the Application for Qualifying Examination form, i.e., equivalent to several courses, together with topics from one the minor areas.

At least one month before the exam date, the student must also prepare and submit the following documents to head graduate adviser: a white program of study card that includes all major and minor courses taken or planned (whether or not they are included in the syllabus), a transcript, a list of faculty members who will serve on the exam committee, a syllabus, a preliminary draft of the technical report for the exam committee, and the student’s adviser’s signature to approve the intended date and topics. Both the Graduate Division's application for qualifying examination form and the program of study card must be approved and signed by the head graduate adviser.

At least two weeks prior to the exam, the student must submit his or her qualifying exam report, to the qualifying exam committee. This report should be in the form of a research proposal, and should include both a substantial survey and critical evaluation of the literature in the likely area of the dissertation, and a potential research agenda in this area. If the student has completed preliminary research in this area, it is also appropriate to include a report of this research in this document. However, preliminary results are not required, and cannot make up the bulk of the document.

The qualifying exam document will be reviewed by the three professors who represent the major on the student’s qualifying examination committee, to determine adequacy of preparation for the research area. For students who follow these guidelines and the recommendations of the graduate adviser and thesis adviser, this usually results in quick approval. However, if preparation is judged to be inadequate, they may recommend additional course work and postponement of this examination.

In many departments, including ours, it has been the practice for students to schedule their own qualifying examinations. This exam is to be scheduled for three hours, at a time when all committee members can attend.

The oral portion of the qualifying examination has two parts. In the first part, the student presents a 45-minute talk based on his or her qualifying examination report. The committee will ask questions pertaining to the report and presentation at this time. During the second part of the oral examination, the committee will ask more general questions to determine the student’s level of expertise in the broadly defined research area specified by the student (and described in the syllabus). During this time, the outside committee member will also ask questions about one of the student’s minor areas.

If the student's performance is judged to be unsatisfactory, the committee may recommend reexamination, possibly after additional preparation has been completed. If the reasons for the unsatisfactory performance are judged to be major and fundamental, the committee may recommend that a second attempt be denied.

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Time in Candidacy

Advancement

After passing the qualifying examination, the student should file an application for advancement to candidacy, which sets up a three-person guidance committee for the dissertation. Once this is approved, the student is eligible for reduced fees. After advancing to candidacy, the student is expected to spend full time doing research on his or her dissertation, and on related teaching tasks.

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Required Professional Development

Teaching Opportunities

The Department of IEOR strives to provide every student with an opportunity to gain teaching experience. Every year, students work as teaching assistants responsible for discussion or laboratory sections (Graduate Student Instructors, or GSIs) and serve as readers assisting with grading but not conducting independent teaching.

Professional Conference Attendance

Workshops

At least once a year after passing the qualifying examination, the student is required to hold a dissertation workshop. Each dissertation workshop has two primary objectives:

1. It provides the department an opportunity to review the progress of students who have passed the qualifying examination, toward completion of their doctoral dissertation.
2. It facilitates interaction between the student and the dissertation committee and provides the basis for useful and consistent guidance. While the dissertation committee is primarily responsible for providing guidance, feedback from other faculty and from students is sought as well.

During the workshop, the candidate is expected to present a prospective of, and results from, the dissertation research. Dissertation advisers should advise students about the appropriate time for the workshops. However, initiation of the workshops is the student's responsibility. The student needs to notify the department at least one month in advance of the desired workshop date, and coordinate this date with the dissertation committee. At least two weeks prior to each workshop, the student shall distribute to the dissertation committee a report called the dissertation prospectus. Announcement of the workshop will be made through all the channels used to announce departmental seminars.

Each workshop is divided into two parts. The first part is devoted to a public presentation by the student and subsequent discussion. This part is conducted as a seminar and is open to all faculty and students. Graduate students and faculty who have research interests that relate to the workshop are encouraged to attend; this may be their best opportunity to provide constructive feedback to the candidate. (Graduate students who have not yet reached this stage in their own program often find that participating in workshops is a valuable educational experience.) The dissertation committee moderates the presentation and discussion, controls the asking of questions by the audience, and calls an end to the first part of the workshop.

In the second part of the workshop, which immediately follows the public presentation, the dissertation committee and other interested faculty members will reconvene in private with the candidate for the purpose of giving more feedback and specific guidelines for continuing research. At this time, the committee may decide that the candidate's progress is unsatisfactory. Should the committee reach this conclusion, it will be reported in writing, with proper justification, to the candidate and the department chair. The committee may require an additional workshop sooner than one year after the unsatisfactory one. Recurrent failure to present a satisfactory prospectus workshop may result in disqualification of the student and termination of doctoral candidacy.

Dissertation Defense Workshop

Once the candidate has completed his or her research and completely written the thesis, a defense workshop must be scheduled and held. A completed copy of the thesis must be distributed to the committee at least two weeks before this final workshop. This workshop will follow the same format as other workshops. The committee will inform the candidate about any remaining problems or issues with the thesis. If the committee has serious issues with the thesis, they may require an additional defense workshop.

Master's Degree Requirements (MS)

Unit Requirements

Students are required to complete 24 semester units of coursework, 12 units of which must be graduate courses in the major taken for a letter grade. IND ENG 298 units do not count towards this requirement.

Curriculum

All students are required to take 1 unit of IND ENG 298; at least one course each from the following categories: Optimization, Stochastic Models, and Modeling (see below); and additional courses.

Beyond these requirements, the program is quite flexible. No more than two units of IND ENG 299 may be counted toward the degree. The remainder of the program can include electives outside the department. Entering students are expected to have two years of undergraduate mathematics, primarily calculus but including linear algebra. In addition, they are expected to have completed at least one semester each of upper division courses in probability and in statistics. They should also have competency in a scientific programming language.

The requirements for each concentration follow the course lists for the three categories, below.

Course List

Code	Title	Units
Optimization courses
IND ENG 160	Nonlinear and Discrete Optimization	3
IND ENG 162	Linear Programming and Network Flows	3
IND ENG S162	Linear Programming	3
IND ENG 262A	Mathematical Programming I	4
or EL ENG C227T	Introduction to Convex Optimization
IND ENG 262B	Mathematical Programming II	3
IND ENG 264	Computational Optimization	3
IND ENG 266	Network Flows and Graphs	3
IND ENG 269	Integer Programming and Combinatorial Optimization	3

Course List

Code	Title	Units
Stochastic Models courses
IND ENG 131	Discrete Event Simulation	3
IND ENG 165	Engineering Statistics, Quality Control, and Forcasting	3
IND ENG 166	Decision Analytics	3
IND ENG 173	Introduction to Stochastic Processes 1	3
IND ENG 231	Introduction to Data Modeling, Statistics, and System Simulation	3
IND ENG 261	Experimenting with Simulated Systems	3
IND ENG 263A	Applied Stochastic Process I	4
IND ENG 263B	Applied Stochastic Process II	3
IND ENG 267	Queueing Theory	3
IND ENG 268	Applied Dynamic Programming	3

Course List

Code	Title	Units
Modeling courses
IND ENG 150	Production Systems Analysis	3
IND ENG 153	Logistics Network Design and Supply Chain Management	3
IND ENG 215	Analysis and Design of Databases	3
IND ENG 220	Economics and Dynamics of Production	3
IND ENG 221	Introduction to Financial Engineering	3
IND ENG 250	Introduction to Production Planning and Logistics Models	3
IND ENG 251	Facilities Design and Logistics	3
IND ENG 253	Supply Chain Operation and Management	3
IND ENG 254	Production and Inventory Systems	3
IND ENG 265	Learning and Optimization	3
IND ENG 268	Applied Dynamic Programming	3
IND ENG 290A	Dynamic Production Theory and Planning Models	3
IND ENG 290R	Topics in Risk Theory	3

Course Requirements by Concentration

Operations Research Concentration

Course List

Code	Title	Units
IND ENG 262A	Mathematical Programming I (fulfills Optimization requirement)	4
IND ENG 263A	Applied Stochastic Process I (fulfills Stochastic Models requirement)	4
IND ENG 298	Group Studies, Seminars, or Group Research	1
Select two from the following (at least one must be a Modeling course):
IND ENG 221	Introduction to Financial Engineering
IND ENG 261	Experimenting with Simulated Systems
IND ENG 262B	Mathematical Programming II
IND ENG 263B	Applied Stochastic Process II
IND ENG 264	Computational Optimization
IND ENG 266	Network Flows and Graphs
IND ENG 267	Queueing Theory
IND ENG 268	Applied Dynamic Programming
IND ENG 269	Integer Programming and Combinatorial Optimization

Production & Service Operations Concentration

When selecting options below, please be sure to select at least one course from each category: Optimization, Stochastic Models, and Modeling (see above).

Course List

Code	Title	Units
IND ENG 298	Group Studies, Seminars, or Group Research	1
Select two of the following:
IND ENG 250	Introduction to Production Planning and Logistics Models
IND ENG 251	Facilities Design and Logistics
IND ENG 254	Production and Inventory Systems
Select one of the following:
IND ENG 150	Production Systems Analysis
IND ENG 151	Service Operations Design and Analysis
IND ENG 153	Logistics Network Design and Supply Chain Management
Select one of the following:
IND ENG 130	Methods of Manufacturing Improvement
IND ENG 131	Discrete Event Simulation
IND ENG 150	Production Systems Analysis (if not select above)
IND ENG 151	Service Operations Design and Analysis (if not selected above)
IND ENG 153	Logistics Network Design and Supply Chain Management (if not selected above)
IND ENG 165	Engineering Statistics, Quality Control, and Forcasting

Simulation & Decision Technology Concentration

Course List

Code	Title	Units
IND ENG 115	Industrial and Commercial Data Systems (fulfills Modeling requirement)	3
or IND ENG 215	Analysis and Design of Databases
IND ENG 261	Experimenting with Simulated Systems (fulfills Stochastic Models requirement)	3
or IND ENG 131	Discrete Event Simulation
IND ENG 298	Group Studies, Seminars, or Group Research	1
Select two of the following (one must be from the Optimization category):
IND ENG 166	Decision Analytics
IND ENG 262A	Mathematical Programming I
or EL ENG C227T	Introduction to Convex Optimization
UGBA 147	Special Topics in Operations and Information Technology Management

Financial Systems Concentration

Course List

Code	Title	Units
IND ENG 131	Discrete Event Simulation (fulfills Stochastic Models requirement)	3
or IND ENG 231	Introduction to Data Modeling, Statistics, and System Simulation
IND ENG 221	Introduction to Financial Engineering (fulfills Modeling requirement)	3
IND ENG 222	Financial Engineering Systems I	3
IND ENG 223	Financial Engineering Systems II	3
IND ENG 298	Group Studies, Seminars, or Group Research	1

The Comprehensive Exam or Project

In addition to course and waiver exam requirements, students are required to complete one of two options: a comprehensive exam or a master's project and oral presentation of this project. The structure of the comprehensive exam may vary from year to year, but is designed so that students whose curriculum includes 12 units of graduate courses in the major and satisfies the group distribution listed above should be prepared to take the exam. At the current time, the comprehensive exam consists of a short oral presentation to a panel of two or three faculty of a solution to a case study, for which the students will be given at least two weeks to prepare, followed by relevant questions from the faculty panel.

Master of Science Plan I (Thesis)

Students may complete the requirements by writing a thesis, rather than taking a comprehensive examination. The course requirements under the thesis option are the same as under the comprehensive option. Under the thesis option, the minimum unit requirement of regular course work is 20 units, not including the thesis. A committee of three professors, including one from outside the IEOR Department, will be formed to guide and approve the thesis.

Relation to Doctoral Requirements

In general, the first year doctoral requirements meet the requirements of the MS degree, but the reverse is not necessarily true. Students who are interested in earning a PhD should apply to enter the MS/PhD if they do not yet have an MS degree. More detailed information on the entrance exam may be found on the Doctoral Degree Requirements tab.

Master's Degree Requirements (MEng)

Unit Requirements

Minimum number of units to complete degree: 24 semester units.

Curriculum

Technical Course work (must be taken for a letter grade):

• Core Courses: All students are required to take IND ENG 240 & 241. Students in the part-time evening Decision Analytics concentration must also take IND ENG 242 and 290.
• Technical Electives: Students must complete a minimum of 6 units of IND ENG 200 level technical electives from the lists below. 

Leadership Courses (must be taken for a letter grade) 

• All students must complete 6 semester units of core leadership courses, which must be in the 200-series.
• The Innovation Lecture Series (IND ENG 295 taken S/U), is optional.

Capstone Project Courses:

• Students must take the capstone integration course each semester.
• All students must complete 5 units of capstone courses: 2 units in the fall semester and 3 units in the spring semester (see the course lists below).

Required Technical Courses 

Course List

Code	Title	Units
IND ENG 240	Optimization Analytics	3
IND ENG 241	Risk Modeling, Simulation, and Data Analysis	3

Fall Technical Electives

Course List

Code	Title	Units
IND ENG 222	Financial Engineering Systems I (Fall Courses)	3
IND ENG 250	Introduction to Production Planning and Logistics Models	3
IND ENG 262A	Mathematical Programming I	4
IND ENG 263A	Applied Stochastic Process I	4
IND ENG 266	Network Flows and Graphs	3

Spring Technical Electives

Course List

Code	Title	Units
IND ENG 220	Economics and Dynamics of Production	3
IND ENG 221	Introduction to Financial Engineering	3
IND ENG C227B	Convex Optimization and Approximation	3
IND ENG 242	Applications in Data Analysis	3
IND ENG C253	Supply Chain and Logistics Management	3
IND ENG 262B	Mathematical Programming II	3
IND ENG 265	Learning and Optimization	3
IND ENG 267	Queueing Theory	3
IND ENG 290	Special Topics in Industrial Engineering and Operation Research (Fundamentals of Machine Learning & Data Analytics)	3
IND ENG 290R	Topics in Risk Theory (Portfolio and Risk Analytics)	3

Required Technical Electives (Decision Analytics Concentration Only)

Course List

Code	Title	Units
IND ENG 242	Applications in Data Analysis	3
IND ENG 290	Special Topics in Industrial Engineering and Operation Research (Fundamentals of Machine Learning & Data Analytics)	3

Required Leadership Courses

Course List

Code	Title	Units
ENGIN 271	Engineering Leadership I (Fall)	3
ENGIN 272	Engineering Leadership II (Spring)	3

Required Capstone Courses

Course List

Code	Title	Units
ENGIN 295	Communications for Engineering Leaders (Fall and Spring)	1
ENGIN 296MA	Master of Engineering Capstone Project (Fall)	2
ENGIN 296MB	Master of Engineering Capstone Project (Spring)	3

Courses

Industrial Engineering and Operations Research

Faculty and Instructors

Faculty

Ilan Adler, Professor. Financial engineering, optimization theory, combinatorial probability models.
Research Profile

Anil Jayanti Aswani, Assistant Professor.

Alper Atamturk, Professor. Logistics, integer programming, computational optimization, robust optimization.
Research Profile

Laurent El Ghaoui, Professor. Decision-making under uncertainty, convex optimization, robust solutions, semidefinite programming, exhaustive simulation.
Research Profile

Lee Fleming, Professor. Invention, innovation, patents, big data, leadership.
Research Profile

Ken Goldberg, Professor. Robotics, art, social media, new media, automation.
Research Profile

Xin Guo, Professor. Financial engineering, industrial engineering and operations, stochastic processes and applications, stochastic control, semi-martingale and filteration expansions, credit risk, (ir)reversible investment.
Research Profile

Dorit S. Hochbaum, Professor. Data mining, integer programming, discrete optimization, network flow techniques, clustering, image segmentation, machine vision, pattern recognition.
Research Profile

Philip M. Kaminsky, Professor. Biotechnology, logistics, distribution, algorithms, planning, optimization, control, manufacturing, semiconductors, scheduling, biomanufacturing, probabilistic methods, production scheduling, supply chain management, operations management, logistic.
Research Profile

Javad Lavaei, Assistant Professor.

Robert C. Leachman, Professor. Logistics, manufacturing, semiconductors, scheduling, supply chain systems, dynamic production models, production planning and scheduling.
Research Profile

Shmuel S. Oren, Professor. Economics, algorithms, financial engineering, risk management, planning, optimization, operation of electric power systems, market based coordination of network systems, trading instruments.
Research Profile

Christos H. Papadimitriou, Professor. Economics, evolution., algorithms, game theory, networks, optimization, complexity.
Research Profile

Rhonda L. Righter, Professor. Modeling, optimization, stochastic systems, systems with uncertainty.
Research Profile

Lee W. Schruben, Professor. Health care systems, simulation, optimization of simulation system response, foundations of simulation modeling, supply chains, experimental designs, biopharmaceuticals, Production.
Research Profile

Zuo-Jun Shen, Professor. Logistics, supply chain design and management, inventory management, auction mechanism design.
Research Profile

Ikhlaq Sidhu, Adjunct Professor. Technology management, industrial engineering and operations, technology commerialization, interdisciplinary engineering.
Research Profile

Candace Yano, Professor. Inventory control, production planning, distribution systems planning, integrated production-quality models, integrated manufacturing-marketing models.
Research Profile

Lecturers

Solomon Darwin, Lecturer.

Nicholas L. Gunther, Lecturer.

Han Jin, Lecturer.

Tal Lavian, Lecturer.

David Law, Lecturer.

Ronald Lesniak, Lecturer.

Mehdi Maghsoodnia, Lecturer.

Deepak Rajan, Lecturer.

Kenneth Sandy, Lecturer.

Ken Singer, Lecturer.

Naeem Zafar, Lecturer.

Emeritus Faculty

Richard E. Barlow, Professor Emeritus. Industrial engineering and operations, reliability theory, statistical data analysis, Bayesian probability modeling.
Research Profile

Stuart E. Dreyfus, Professor Emeritus. Neural networks, dynamic programming, limits of operations research modeling, cognitive ergonomics.
Research Profile

C. Roger Glassey, Professor Emeritus. Simulation of manufacturing systems, production planning & scheduling, mathematical optimization.
Research Profile

Robert M. Oliver, Professor Emeritus. Risk management, operations research, industrial engineering, prediction of rare events, default and fraud detection, credit risk scoring, analysis tools, computer software, acquisition and negotiation strategies.
Research Profile

Sheldon M. Ross, Professor Emeritus. Financial engineering, simulations, stochastics, statistical analysis.
Research Profile

J. George Shanthikumar, Professor Emeritus. Scheduling, production system modelling & analysis, queueing theory & applications, reliability & probability theory, sequencing, simulation methodology, stochastic processes & modelling.
Research Profile

Ronald W. Wolff, Professor Emeritus. Stochastic processes, queueing theory, queuing network, transmission systems.
Research Profile